IMPROVED METHOD FOR STEREO VISION-BASED HUMAN DETECTION FOR A MOBILE ROBOT FOLLOWING A TARGET PERSON
DOI:
https://doi.org/10.7166/26-1-891Keywords:
Human Detection, Human Tracking, Service Robot, Haar cascade classifier, Mean Shift, LK Optical Flow, Particle Filter, Kalman Filter, Stereo VisionAbstract
Interaction between humans and robots is a fundamental need for assistive and service robots. Their ability to detect and track people is a basic requirement for interaction with human beings. This article presents a new approach to human detection and targeted person tracking by a mobile robot. Our work is based on earlier methods that used stereo vision-based tracking linked directly with Hu moment-based detection. The earlier technique was based on the assumption that only one person is present in the environment – the target person – and it was not able to handle more than this one person. In our novel method, we solved this problem by using the Haar-based human detection method, and included a target person selection step before initialising tracking. Furthermore, rather than linking the Kalman filter directly with human detection, we implemented the tracking method before the Kalman filter-based estimation. We used the Pioneer 3AT robot, equipped with stereo camera and sonars, as the test platform.
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